
import pandas as pd
import numpy as np
import seaborn as sns
from plotly.subplots import make_subplots
import plotly.express as px
import colorama
from colorama import Fore
import matplotlib.pyplot as plt
from termcolor import colored


df = pd.read_csv('movie_data_preprocessed.csv')


#search by genre
l = []
for i in df.Genre:
    for j in i.split(","):
        l.append(j.strip())
Available_Genres = list(set(l))

def genre_seach(s,df):
    if len(s)==3:
        a = s[0]
        b = s[1]
        c = s[2]
        data = df.iloc[:,:9].loc[(df[a]==True) & (df[b]==True) & (df[c]==True)].sort_values('Rating', ascending = False)
    elif len(s)==2:
        a = s[0]
        b = s[1]
        data = df.iloc[:,:9].loc[(df[a]==True) & (df[b]==True)].sort_values('Rating', ascending = False)
    elif len(s)==1:
        a = s[0]
        data = df.iloc[:,:9].loc[df[a]==True].sort_values('Rating', ascending = False)
    return data.set_index('Title')


#Actor Search search upto 4 actor simultaniously
def actor_search(df,s,n):
    if len(s)<=4:
        for i in range(0,(4-len(s))):
            s.append('Vola')
            
    a = df.loc[(df['actor_1'] == s[0]) | (df['actor_1'] == s[1]) | (df['actor_1'] == s[2]) | (df['actor_1'] == s[3])].index
    b = df.loc[(df['actor_2'] == s[0]) | (df['actor_2'] == s[1]) | (df['actor_2'] == s[2]) | (df['actor_2'] == s[3])].index
    c = df.loc[(df['actor_3'] == s[0]) | (df['actor_3'] == s[1]) | (df['actor_3'] == s[2]) | (df['actor_3'] == s[3])].index
    d = df.loc[(df['actor_4'] == s[0]) | (df['actor_4'] == s[1]) | (df['actor_4'] == s[2]) | (df['actor_4'] == s[3])].index
    l = a.append(b).append(c).append(d)
    a = list(set(l))
    if n==1:
        print('Total Number of Movies : ',len(a))
        return df.iloc[:,:9].iloc[a].sort_values('Rating', ascending = False)
    else:
        return df.iloc[:,:9].iloc[a].sort_values('Rating', ascending = False)


#search by combining actors(all in one movie)
def actor_combination(df,b):
    ok = df.copy()
    df.Stars = df.Stars.apply(lambda x: x.title())
    c = []
    star = df.Stars.tolist()
    for i in df.Stars:
        if len(i.split(','))==4:
            a = []
            a.append(i.split(',')[0].strip())
            a.append(i.split(',')[1].strip())
            a.append(i.split(',')[2].strip())
            a.append(i.split(',')[3].strip())
            if len(set(b)-set(a)) == 0:
                c.append(star.index(i))
            else:
                del a
        else:
            a = []
            a.append(i.split(',')[0].strip())
            a.append(i.split(',')[1].strip())
            a.append(i.split(',')[2].strip())
            if len(set(b)-set(a)) == 0:
                c.append(star.index(i))
            else:
                del a
    print('Total Number of Movies : ',len(c))
    return ok.iloc[:,:9].iloc[c].sort_values('Rating', ascending = False).set_index('Title')


#search movie by director
def director_search(df,l):
    b = 0
    a = []
    for i in df.Directors:
        if len(set(l)-set([x.strip() for x in i.split(',')])) == 0:
            a.append(b)
        b = b+1
    print('Total Number of Movies : ',len(a))
    return df.iloc[:,:9].iloc[a].sort_values('Rating', ascending = False).set_index('Title')


a = []
b = []
c = []
for i in df.Main_Directors.unique():
    a.append(len(df.loc[df['Main_Directors']==i]))
    c.append(round(np.mean(df['Rating'].loc[df['Main_Directors']==i]),2))
    b.append(i)
ind = pd.Index(list(range(1,len(a)+1)))
data = pd.DataFrame(zip(b,a,c),columns = ['Director','Movie_count','Avg_Rating']).sort_values('Movie_count', ascending = False).set_index(ind)
print('\n')
str1 = '~'*15+' The Most busy Director '+'~'*15
print(Fore.CYAN + colored(str1, attrs=['bold']))
display(data.head(10))


~~~~~~~~~~~~~~~ The Most busy Director ~~~~~~~~~~~~~~~


#Movies with HIghest LOss and Gains
dfn = df[['Title','Rating','Main_Directors','new_budget','new_Income']].loc[df['new_Income']>0]
dfn['new_budget'] = round(dfn['new_budget']/1000000,2)
dfn['new_Income'] = round(dfn['new_Income']/1000000,2)
dfn['Amount_Gained'] = dfn['new_Income']-dfn['new_budget']
#dfn.head(10)
a = dfn.sort_values('Amount_Gained', ascending = False).reset_index().drop('index',axis=1)
b = dfn.sort_values('Amount_Gained').reset_index().drop('index',axis=1)
a.index += 1 
b.index += 1 
print('\n')
str1 = '~'*15+' Movies with biggest gains '+'~'*15
print(Fore.GREEN + colored(str1, attrs=['bold']))
display(a.head(10))
print('\n')
str1 = '~'*15+' Movies with biggest losses '+'~'*15
print(Fore.RED + colored(str1, attrs=['bold']))
display(b.head(10))


~~~~~~~~~~~~~~~ Movies with biggest gains ~~~~~~~~~~~~~~~


~~~~~~~~~~~~~~~ Movies with biggest losses ~~~~~~~~~~~~~~~


dfn = df.iloc[:,15:34].astype(int)
a = []
b = []
for i in dfn.columns:
    a.append(dfn[i].sum())
    b.append(i)
dfn = pd.DataFrame(zip(b,a),columns = ['Genre','Count']).sort_values('Count',ascending = False).reset_index().drop('index',axis=1)
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots(figsize=(10, 5))
sns.color_palette("tab10")
my_plot = sns.barplot(y = dfn['Count'], x = dfn['Genre'])
my_plot.set_xticklabels(my_plot.get_xticklabels(), rotation=45)
ax.bar_label(ax.containers[0])
my_plot.set(xlabel=None)
my_plot.set_title('Movies in current decade per Genre')

Text(0.5, 1.0, 'Movies in current decade per Genre')


ok = df.iloc[:,:9].query("Runtime > 0").sort_values('Runtime', ascending = False)

fig1 = px.bar(ok.head(5), y='Runtime', x='Title',
            title="Movies by Runtime",
            color = 'Genre', text = 'Title')
fig2 = px.bar(ok.tail(5), y='Runtime', x='Title',
            color = 'Genre', text = 'Title',)
fig1.update_xaxes(visible=False)
fig2.update_xaxes(visible=False)


fig1.show()
fig2.show()
print('\n')
str1 = '~'*45+' Top Loooonng Movies '+'~'*45
print(Fore.RED + colored(str1, attrs=['bold']))
display(df.iloc[:,:9].sort_values('Runtime', ascending = False).head(5).sort_values('Rating',ascending = False))
print('\n')
str1 = '~'*45+' The Shortest Movies '+'~'*45
print(Fore.GREEN + colored(str1, attrs=['bold']))
display(df.iloc[:,:9].query("Runtime > 0").sort_values('Runtime').head(5).sort_values('Rating',ascending = False))


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Top Loooonng Movies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The Shortest Movies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


import squarify
a = df['Filming_location'].loc[df['Filming_location'] != 'Unknown'].value_counts().head(10)
plt.figure(figsize=(12,5))
squarify.plot(sizes=a.values.tolist(), label=a.index.tolist(), value=a.values.tolist(),
              color=[plt.cm.Set2(i) for i in range(7)],
              text_kwargs={'fontsize': 13.8})
plt.title('TOP 10 Country Praduction', fontsize=20,fontweight='bold')
plt.axis('off')
plt.show()


a = df.Month.value_counts().index
b = df.Month.value_counts().values
data = pd.DataFrame(zip(a,b),columns = ['Month','count'])
data = data.iloc[[8,11,6,10,9,3,4,1,5,2,0,1]]
fig, ax = plt.subplots(figsize=(12, 5))
my_plot = sns.barplot(y = data['Month'],x = data['count'])
my_plot.bar_label(ax.containers[0])
my_plot.set(xlabel=None, ylabel=None)
my_plot.set_title('Movies praduced montly around the decade')

Text(0.5, 1.0, 'Movies praduced montly around the decade')


from tabulate import tabulate
def print_tabular(ok,c1,c2):
    q = [i[0] for i in ok.index]
    q1 = [i for i in ok]
    return print (tabulate(zip(q,q1), headers=[c1, c2]))


l = []
for i in df.Stars:
    for a in i.split(','):
        l.append(a.strip())
dfn = pd.DataFrame(l)
print('\n')
str1 = '~'*10+' Top 10 Busy Stars of the Decade '+'~'*10
print(Fore.RED + colored(str1, attrs=['bold']))

ok = dfn.value_counts().head(10)
print_tabular(ok,"Actor","Movie_Count")


~~~~~~~~~~ Top 10 Busy Stars of the Decade ~~~~~~~~~~
Actor                 Movie_Count
------------------  -------------
Dwayne Johnson                 25
Mark Wahlberg                  24
Ryan Reynolds                  23
Brad Pitt                      22
Amy Adams                      22
Matt Damon                     22
Scarlett Johansson             22
Liam Neeson                    21
Tom Hanks                      21
Cate Blanchett                 21


dfn = df[['Title','Rating','new_budget','Filming_location','origin_count']].sort_values('origin_count', ascending = False).head(10).reset_index().drop('index', axis =1)
dfn['Budget_Million'] = dfn['new_budget']/1000000
print('\n')
str1 = '~'*40+' Top Multi-Origin Movies '+'~'*40
print(Fore.BLUE + colored(str1, attrs=['bold']))
dfn.drop('new_budget',axis =1)


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Top Multi-Origin Movies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


dfn = df.loc[df['Animation']==True].sort_values('new_Income', ascending = False).sort_values('Rating', ascending = False).reset_index().drop('index',axis=1)
print('\n')
str1 = '~'*50+' Top 12 Animation Movies '+'~'*50
print(Fore.YELLOW + colored(str1, attrs=['bold']))
dfn.iloc[:,:9].head(12)


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Top 12 Animation Movies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


a,r,g,bug = ([] for i in range(4))
for n,i in enumerate(df.Stars):
  for j in i.split(','):
    a.append(j.strip())
    r.append(df.Rating.iloc[n])
    g.append(df.Genre.iloc[n])
    bug.append(df.new_budget.iloc[n])
ns = pd.DataFrame(zip(a,r,g,bug), columns = ['Star','Rating','Genre','Budget'])
#ns.shape
a,b,c,d = ([] for i in range(4))
for i in ns.Star.unique():
    a.append(ns['Genre'].loc[ns['Star']==i].tolist())
    b.append(i)
    c.append(round(ns['Rating'].loc[ns['Star']==i].mean(),2))
    d.append(round(ns['Budget'].loc[ns['Star']==i].mean()/1000000,2))
ns1 = pd.DataFrame(zip(b,c,a,d), columns = ['Star','Rating','Genre','Budget'])    
#ns1.sample(3)
b,a,v,q = ([] for i in range(4))
for i in ns1['Genre']:
    a = []
    for x in i:
        for j in x.split(','):            
            a.append(j.strip())
    b.append(list(set(a)))
    v.append(len(set(a)))
    
for s in ns1.Star:
    q.append(len(actor_search(df,[s],0)))
    
ns1['All_Genre'] = b
ns1['Genre_Count'] = v
ns1['Movie_Count'] = q
del ns
ns1.drop('Genre',axis =1, inplace = True)
display(ns1.shape)
#ns1.sample(5)

(3502, 6)


from IPython.display import Image
Image(filename='actor_award.gif')

<IPython.core.display.Image object>


from IPython.display import Image
Image(filename='R.png')


from pandas import option_context
with option_context('display.max_colwidth', None):
    print("Budget in Million $")
    print('\n')
    str2 = '~'*45+' Actors Trusted with High Budget '+'~'*45
    print(Fore.MAGENTA + colored(str2, attrs=['bold']))
    display(ns1.sort_values(by = 'Budget', ascending = False).head(5).set_index("Star").T)
    print('\n')
    str1 ='~'*45+' Most Varsatile Actor '+'~'*45
    print(Fore.CYAN + colored(str1, attrs=['bold']))
    display(ns1.sort_values(by = 'Genre_Count', ascending = False).head(1).set_index("Star").T)
    print('\n')
    str3 = '~'*45+' Actor with maax Movies '+'~'*45
    print(Fore.RED + colored(str3, attrs=['bold'])) 
    display(ns1.sort_values(by = 'Movie_Count', ascending = False).head(1).set_index("Star").T)
    print('\n')
    str4 = '~'*45+' Most consistent Performance '+'~'*45
    print(Fore.GREEN + colored(str4, attrs=['bold']))
    display(ns1.loc[ns1['Movie_Count']>10].sort_values(by = 'Rating', ascending = False).head(1).set_index("Star").T)

Budget in Million $


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Actors Trusted with High Budget ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most Varsatile Actor ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Actor with maax Movies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most consistent Performance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


from pandas import option_context
with option_context('display.max_colwidth', None):
    print("Budget in Million $")
    print('\n')
    str2 = '~'*15+' Actors Trusted with High Budget '+'~'*15
    print(Fore.MAGENTA + colored(str2, attrs=['bold']))
    display(ns1.sort_values(by = 'Budget', ascending = False).head(5).set_index("Star").T)

Budget in Million $


~~~~~~~~~~~~~~~ Actors Trusted with High Budget ~~~~~~~~~~~~~~~


print('\n')
str4 = '~'*15+' Most consistent Performance '+'~'*15
print(Fore.GREEN + colored(str4, attrs=['bold']))
display(ns1.loc[ns1['Movie_Count']>10].sort_values(by = 'Rating', ascending = False).head(1).set_index("Star").T)


~~~~~~~~~~~~~~~ Most consistent Performance ~~~~~~~~~~~~~~~


from IPython.display import Image
Image(filename='gains.png')


x = df['Rating']
y = df['new_Income']/1000000
z = df['new_budget']/1000000


c = [i.split(',')[0] for i in df['Genre']]
fig = px.scatter(x=z, y=y, title='Gains over Budget comparision of all the movies ', color=c,
                 width=750, height=600)
fig.update_layout(
    xaxis_title="Budget",
    yaxis_title="Income",
    legend_title="Genres",)

import seaborn as sns
plt.figure(figsize=(8,6))
palette = sns.color_palette("tab10", as_cmap=True)
#display(sns.lineplot(y=y,x=x))
#display(sns.lineplot(y=z,x=x))
sns.lineplot(x=z,y=y,palette=palette)
fig.show()

C:\Users\rishi\AppData\Local\Temp\ipykernel_15572\682156227.py:19: UserWarning:

Ignoring `palette` because no `hue` variable has been assigned.


sns.lineplot(x=z,y=y)
plt.figure(figsize=(8,6))

<Figure size 800x600 with 0 Axes>

<Figure size 800x600 with 0 Axes>


#Making Search Algorithms ever easier
def search_(s):
    l = []
    for j in s.split(','):
        i = j.strip()
        l.append(i.title())
    return l
def search_in_genre(s):
    l = search_(s)
    return genre_seach(l,df)
def search_by_actor(s):
    b = search_(s)
    return actor_combination(df,b)
def search_by_director(s):
    return actor_combination(df,search_(s))


# List of all available Genres
l = []
for i in df.Genre:
    for j in i.split(","):
        l.append(j.strip())
Available_Genres = list(set(l))
ind = list(range(1,len(Available_Genres)+1))
print(tabulate(zip(ind,Available_Genres), headers=['S.No.','List of Available Genres'], tablefmt="grid"))

+---------+----------------------------+
|   S.No. | List of Available Genres   |
+=========+============================+
|       1 | Thriller                   |
+---------+----------------------------+
|       2 | Romance                    |
+---------+----------------------------+
|       3 | Biography                  |
+---------+----------------------------+
|       4 | Family                     |
+---------+----------------------------+
|       5 | Animation                  |
+---------+----------------------------+
|       6 | History                    |
+---------+----------------------------+
|       7 | Musical                    |
+---------+----------------------------+
|       8 | Drama                      |
+---------+----------------------------+
|       9 | Crime                      |
+---------+----------------------------+
|      10 | War                        |
+---------+----------------------------+
|      11 | Sci-Fi                     |
+---------+----------------------------+
|      12 | Adventure                  |
+---------+----------------------------+
|      13 | Action                     |
+---------+----------------------------+
|      14 | Music                      |
+---------+----------------------------+
|      15 | Fantasy                    |
+---------+----------------------------+
|      16 | Sport                      |
+---------+----------------------------+
|      17 | Horror                     |
+---------+----------------------------+
|      18 | Western                    |
+---------+----------------------------+
|      19 | Mystery                    |
+---------+----------------------------+
|      20 | Comedy                     |
+---------+----------------------------+


search_in_genre('drama')
search_by_actor('Leonardo Dicaprio')

Total Number of Movies :  13


search_by_director('Sean Penn')

Total Number of Movies :  5


search_in_genre('adventure, biography')


search_by_actor('Leonardo Dicaprio, Christoph Waltz')

Total Number of Movies :  1

	Director	Movie_count	Avg_Rating
1	Ridley Scott	13	6.89
2	Steven Spielberg	12	7.20
3	Antoine Fuqua	11	6.57
4	Shawn Levy	10	6.34
5	Michael Bay	10	6.31
6	Clint Eastwood	10	7.63
7	Zack Snyder	9	6.87
8	Guy Ritchie	9	7.13
9	Tim Burton	9	6.94
10	Ron Howard	8	6.99

	Title	Rating	Main_Directors	new_budget	new_Income	Amount_Gained
1	Avatar	7.8	James Cameron	237.0	2922.92	2685.92
2	Avengers: Endgame	8.4	Anthony Russo	356.0	2797.50	2441.50
3	Avatar: The Way of Water	7.8	James Cameron	350.0	2267.95	1917.95
4	Star Wars: Episode VII - The Force Awakens	7.8	J J Abrams	245.0	2069.52	1824.52
5	Avengers: Infinity War	8.4	Anthony Russo	321.0	2048.36	1727.36
6	Spider-Man: No Way Home	8.3	Jon Watts	200.0	1917.43	1717.43
7	Jurassic World	6.9	Colin Trevorrow	150.0	1671.54	1521.54
8	The Lion King	6.8	Jon Favreau	260.0	1663.25	1403.25
9	Furious 7	7.1	James Wan	190.0	1515.34	1325.34
10	Top Gun: Maverick	8.4	Joseph Kosinski	170.0	1488.73	1318.73

	Title	Rating	Main_Directors	new_budget	new_Income	Amount_Gained
1	The Gray Man	6.5	Anthony Russo	200.0	0.45	-199.55
2	The Tomorrow War	6.5	Chris McKay	200.0	14.40	-185.60
3	Red Notice	6.3	Rawson Marshall Thurber	160.0	0.18	-159.82
4	The Irishman	7.8	Martin Scorsese	159.0	0.97	-158.03
5	Pinocchio 2	5.1	Robert Zemeckis	150.0	0.04	-149.96
6	Mulan	5.7	Niki Caro	200.0	69.97	-130.03
7	White Noise	6.6	Noah Baumbach	80.0	0.07	-79.93
8	Don't Look Up	7.2	Adam McKay	75.0	0.79	-74.21
9	How Do You Know	5.4	James L Brooks	120.0	48.67	-71.33
10	Devotion	7.0	J D Dillard	90.0	19.95	-70.05

	Title	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
1903	The Lord of the Rings: The Return of the King	9.0	2003	December	PG-13	201	Peter Jackson	Elijah Wood, Viggo Mortensen, Ian McKellen, Or...	Action, Adventure, Drama
123	Zack Snyder's Justice League	8.0	2021	March	R	242	Zack Snyder	Henry Cavill, Ben Affleck, Gal Gadot, Amy Adams	Action, Adventure, Fantasy
315	The Irishman	7.8	2019	November	R	209	Martin Scorsese	Robert De Niro, Al Pacino, Joe Pesci, Harvey K...	Biography, Crime, Drama
0	Avatar: The Way of Water	7.8	2022	December	PG-13	192	James Cameron	Sam Worthington, Zoe Saldana, Sigourney Weaver...	Action, Adventure, Fantasy
1564	Grindhouse	7.5	2007	April	R	191	Robert Rodriguez, Eli Roth, Quentin Tarantino,...	Kurt Russell, Rose McGowan, Danny Trejo, Zo Bell	Action, Horror, Thriller

	Title	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
995	Banshee	8.5	2013	September	Unrated	50	Gemma Mc Carthy	Jonathan O Dwyer, Sean Flood, Frank Hurley, Fi...	Action
477	The VelociPastor	5.0	2018	August	Unrated	75	Brendan Steere	Greg Cohan, George Schewnzer, Janice Young, Da...	Action, Adventure, Comedy
1831	9 Songs	4.8	2004	March	Unrated	71	Michael Winterbottom	Kieran O Brien, Margo Stilley, Robert Levon Be...	Drama, Music, Romance
1454	Superhero Movie	4.6	2008	March	PG-13	75	Craig Mazin	Drake Bell, Leslie Nielsen, Sara Paxton, Chris...	Action, Comedy, Sci-Fi
578	Picture of Beauty	3.4	2017	April	Unrated	70	Maxim Ford	Taylor Sands, Danielle Rose, Pawel Hajnos, Mag...	Drama, Romance

First, Importing Important Libraries¶

The daframe used here has been preprocessed and cleaned earlier making it simpler to read and visualize the data¶

The search Algorithms to enable searches based on:¶

Actor Name¶

Search by Director Name¶

Search by Genres¶

Visualizing the data¶

Below Shows the list of Most Busy Directors of the decade¶

Profit & Loss¶

Genre Distribution¶

Long and short Movies¶

Different Country Contribution to the movies within the Dataframe¶

Mounthly release¶

The Busy Actors of the deccade¶

Movies with multiple origins are below¶

TOP Animated movies of the decade¶

BEST ACTORs by Data Science:-¶

How The Income can be compared to the budget of a movie¶

Embeded Search function¶

Search Multiple Genre¶

List of all available Genrese¶

Search Exmaples¶

Click to view in the notebook button¶

	Title	Rating	Filming_location	origin_count	Budget_Million
0	Triangle of Sadness	7.6	Greece	10	10.70
1	Dogville	8.0	Sweden	9	10.00
2	Assassin's Creed	5.6	Spain	8	125.00
3	Valerian and the City of a Thousand Planets	6.4	France	7	177.20
4	The Hitman's Bodyguard	6.9	The Netherlands	7	30.00
5	Kingdom of Heaven	7.2	Morocco	7	130.00
6	Mr. Nobody	7.8	Canada	7	47.00
7	Brimstone	7.0	Hungary	7	12.84
8	Nymphomaniac: Vol. I	6.9	Germany	7	4.70
9	Valkyrie	7.1	Germany	7	75.00

	Title	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
0	WALL·E	8.4	2008	June	G	98	Andrew Stanton	Ben Burtt, Elissa Knight, Jeff Garlin, Fred Wi...	Animation, Adventure, Family
1	Coco	8.4	2017	November	PG	105	Lee Unkrich, Adrian Molina	Anthony Gonzalez, Gael Garc a Bernal, Benjamin...	Animation, Adventure, Comedy
2	Your Name.	8.4	2016	April	PG	106	Makoto Shinkai	Ry nosuke Kamiki, Mone Kamishiraishi, Ry Narit...	Animation, Drama, Fantasy
3	Spider-Man: Into the Spider-Verse	8.4	2018	December	PG	117	Bob Persichetti, Peter Ramsey, Rodney Rothman	Shameik Moore, Jake Johnson, Hailee Steinfeld,...	Animation, Action, Adventure
4	Toy Story 3	8.3	2010	June	G	103	Lee Unkrich	Tom Hanks, Tim Allen, Joan Cusack, Ned Beatty	Animation, Adventure, Comedy
5	Up	8.3	2009	May	PG	96	Pete Docter, Bob Peterson	Edward Asner, Jordan Nagai, John Ratzenberger,...	Animation, Adventure, Comedy
6	Howl's Moving Castle	8.2	2004	June	PG	119	Hayao Miyazaki	Chieko Baish , Takuya Kimura, Tatsuya Gash in,...	Animation, Adventure, Family
7	Demon Slayer the Movie: Mugen Train	8.2	2020	April	R	117	Haruo Sotozaki	Natsuki Hanae, Akari Kit , Yoshitsugu Matsuoka...	Animation, Action, Adventure
8	Finding Nemo	8.2	2003	May	G	100	Andrew Stanton, Lee Unkrich	Albert Brooks, Ellen DeGeneres, Alexander Goul...	Animation, Adventure, Comedy
9	Inside Out	8.2	2015	June	PG	95	Pete Docter, Ronnie Del Carmen	Amy Poehler, Bill Hader, Lewis Black, Mindy Ka...	Animation, Adventure, Comedy
10	How to Train Your Dragon	8.1	2010	March	PG	98	Dean DeBlois, Chris Sanders	Jay Baruchel, Gerard Butler, Christopher Mintz...	Animation, Action, Adventure
11	A Silent Voice: The Movie	8.1	2016	September	Unrated	130	Naoko Yamada	Miyu Irino, Saori Hayami, Aoi Y ki, Kensh Ono	Animation, Drama

Star	Mark Hamill	Carrie Fisher	Daisy Ridley	Brandon Routh	Donald Glover
Rating	6.9	6.9	7.07	6.1	6.85
Budget	317.0	317.0	279.0	270.0	267.5
All_Genre	[Adventure, Action, Fantasy]	[Adventure, Action, Fantasy]	[Adventure, Sci-Fi, Action, Fantasy]	[Adventure, Action, Sci-Fi]	[Sci-Fi, Adventure, Action, Drama, Animation]
Genre_Count	3	3	4	3	5
Movie_Count	1	1	3	1	2

Star	Dwayne Johnson
Rating	6.46
Budget	113.64
All_Genre	[Thriller, Crime, Sci-Fi, Biography, Adventure, Horror, Action, Fantasy, Comedy, Drama, Animation]
Genre_Count	11
Movie_Count	25

Star	Leonardo DiCaprio
Rating	7.84
Budget	96.15
All_Genre	[Crime, Thriller, Romance, Sci-Fi, Biography, Adventure, Western, Action, Mystery, Comedy, Drama]
Genre_Count	11
Movie_Count	13

	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
Title
Inception	8.8	2010	July	PG-13	148	Christopher Nolan	Leonardo DiCaprio, Joseph Gordon Levitt, Ellio...	Action, Adventure, Sci-Fi
The Departed	8.5	2006	October	R	151	Martin Scorsese	Leonardo DiCaprio, Matt Damon, Jack Nicholson,...	Crime, Drama, Thriller
Django Unchained	8.4	2012	December	R	165	Quentin Tarantino	Jamie Foxx, Christoph Waltz, Leonardo DiCaprio...	Drama, Western
The Wolf of Wall Street	8.2	2013	December	R	180	Martin Scorsese	Leonardo DiCaprio, Jonah Hill, Margot Robbie, ...	Biography, Comedy, Crime
Shutter Island	8.2	2010	February	R	138	Martin Scorsese	Leonardo DiCaprio, Emily Mortimer, Mark Ruffal...	Mystery, Thriller
The Revenant	8.0	2015	January	R	156	Alejandro G I rritu	Leonardo DiCaprio, Tom Hardy, Will Poulter, Do...	Action, Adventure, Drama
Blood Diamond	8.0	2006	December	R	143	Edward Zwick	Leonardo DiCaprio, Djimon Hounsou, Jennifer Co...	Adventure, Drama, Thriller
Once Upon a Time in Hollywood	7.6	2019	July	R	161	Quentin Tarantino	Leonardo DiCaprio, Brad Pitt, Margot Robbie, E...	Comedy, Drama
The Aviator	7.5	2004	December	PG-13	170	Martin Scorsese	Leonardo DiCaprio, Cate Blanchett, Kate Beckin...	Biography, Drama
Revolutionary Road	7.3	2008	January	R	119	Sam Mendes	Leonardo DiCaprio, Kate Winslet, Christopher F...	Drama, Romance
Don't Look Up	7.2	2021	December	R	138	Adam McKay	Leonardo DiCaprio, Jennifer Lawrence, Meryl St...	Comedy, Drama, Sci-Fi
The Great Gatsby	7.2	2013	May	PG-13	143	Baz Luhrmann	Leonardo DiCaprio, Carey Mulligan, Joel Edgert...	Drama, Romance
Body of Lies	7.0	2008	October	R	128	Ridley Scott	Leonardo DiCaprio, Russell Crowe, Mark Strong,...	Action, Drama, Thriller

	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
Title
Mystic River	7.9	2003	October	R	138	Clint Eastwood	Sean Penn, Tim Robbins, Kevin Bacon, Emmy Rossum	Crime, Drama, Mystery
21 Grams	7.6	2003	January	R	124	Alejandro G I rritu	Sean Penn, Benicio Del Toro, Naomi Watts, Dann...	Crime, Drama, Thriller
Licorice Pizza	7.2	2021	December	R	133	Paul Thomas Anderson	Alana Haim, Cooper Hoffman, Sean Penn, Tom Waits	Comedy, Drama, Romance
The Tree of Life	6.8	2011	May	PG-13	139	Terrence Malick	Brad Pitt, Sean Penn, Jessica Chastain, Hunter...	Drama, Fantasy
Gangster Squad	6.7	2013	January	R	113	Ruben Fleischer	Sean Penn, Ryan Gosling, Emma Stone, Giovanni ...	Action, Crime, Drama

	Rating	Year	Month	Certificate	Runtime	Directors	Stars	Genre
Title
Into the Wild	8.1	2007	October	R	148	Sean Penn	Emile Hirsch, Vince Vaughn, Catherine Keener, ...	Adventure, Biography, Drama
Papillon	7.2	2017	August	R	133	Michael Noer	Charlie Hunnam, Damijan Oklopdzic, Christopher...	Adventure, Biography, Crime
Everest	7.1	2015	September	PG-13	121	Baltasar Korm kur	Jason Clarke, Ang Phula Sherpa, Thomas M Wrigh...	Action, Adventure, Biography
Wild	7.1	2014	December	R	115	Jean Marc Vall e	Reese Witherspoon, Laura Dern, Gaby Hoffmann, ...	Adventure, Biography, Drama
In the Heart of the Sea	6.9	2015	December	PG-13	122	Ron Howard	Chris Hemsworth, Cillian Murphy, Brendan Glees...	Action, Adventure, Biography
The Lost City of Z	6.6	2016	April	PG-13	141	James Gray	Charlie Hunnam, Robert Pattinson, Sienna Mille...	Adventure, Biography, Drama